Research
KumoRFM: A Foundation Model for In-Context Learning on Relational Data
Systems & Infrastructure
Smarter Adaptive Graph Sampling for More Accurate Graph Learning
Graph Transformers
Accelerating Graph Construction for Production Graph Transformer Workloads
Systems & Infrastructure
Large-Scale Training of Graph Transformers - and How the Kumo Training Backend Works
Graph Transformers
Time Series Forecasting with Graph Transformers
Graph TransformersJune 17, 2025
Speeding Up Graph Learning Models with PyG and torch.compile
Systems & InfrastructureApril 28, 2025
Relational Graph Transformers: A New Frontier in AI for Relational Data
Graph TransformersApril 28, 2025
An Introduction to Graph Transformers
Graph TransformersApril 22, 2025
Introduction to Relational Deep Learning (RDL)
Graph TransformersFebruary 5, 2025
RelBench: A Benchmark for Deep Learning on Relational Database
Graph TransformersFebruary 5, 2025
Improving recommendation systems with LLMs and Graph Transformers
Graph TransformersSeptember 6, 2024
Hybrid Graph Neural Networks
Graph TransformersJuly 17, 2024
Predict Player Churn with AI
Applications & ImplementationJuly 11, 2023
Churn Prediction
Applications & ImplementationMarch 5, 2023
Graph Neural Networks (GNNs): Introduction and examples
Graph TransformersOctober 21, 2022
Customer Success Story
See how Reddit achieved its biggest accuracy lift in ads by integrating Kumo’s graph embeddings.
"Huge Feature Lift" Kumo drove Reddit’s biggest ad performance lift ever.
Read customer story
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